Efficient use of fossil fuels is crucial in maintaining a stable power network. A particularly efficient means of transforming this type of fuel into electrical energy is the gas turbine. Gas turbine components operate in a very high temperature environment and under a variety of loading conditions. Deterioration of parts due to thermal fatigue and wear is a real concern. Maintenance is performed to detect and control wear, as well as to repair or replace worn parts as needed to continue to ensure efficient operation.
The performance of a gas turbine is typically monitored by using a variety of different sensors to assess various aspects of its operation (i.e., power sensors, temperature sensors, pressure sensors, etc.). Unfortunately, the sensor readings themselves tend to be relatively noisy, so at times it is difficult to know if a sensor is properly operating.
There have been a variety of approaches in the prior art that have been used to study the problem of detecting faulty sensors. One approach, for example, monitors for three specific types of errors in sensor readings: short faults, constant faults, and noise faults. Traditional statistical methodologies utilizing principal component analysis (PCA) to search for “faulty sensor” signatures in the sensor readings have been undertaken.
These and other approaches to performing fault detection in gas turbine sensors use a threshold-based approach. That is, if a particular sensor reading is above a given threshold, the sensor is declared as “faulty”. While workable, this approach does not take the time component of the sensor's working environment into consideration. Additionally, since there may be a large noise component in some sensor readings, the threshold approach may have a difficult time discerning the difference between noisy data and a faulty sensor. As a result, this threshold-based approach may yield a large number of false positives, which can be translated into unnecessary inspections of the sensors, interrupting the performance of the gas turbine.